2.0 TAI profiles
TAI profiles
From PES, one can obtain the TAI profiles through the
mytTAI::TAI function. The myTAI package, to my
knowledge, does not enable the flexibility to plot the separate sexes,
for example in one plot. Therefore, I will not use the
myTAI::PlotSignature function.
Here, I show the TAI profiles for the developmental stages. For
tissues of the adult (matSP) Fucus species, one can find them
in 2.1 TAI tissues in Fucus matSP.
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(myTAI)Load PES
Non-transformed
Fd_PES <-
readr::read_csv(file = "data/Fd_PES.csv")## Rows: 7898 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (8): Phylostratum, gamete, E1, E2, E3, E4, E5, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fd_PES_F <-
readr::read_csv(file = "data/Fd_PES_F.csv")## Rows: 7898 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (2): Phylostratum, gamete
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fd_PES_M <-
readr::read_csv(file = "data/Fd_PES_M.csv")## Rows: 7898 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (2): Phylostratum, gamete
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fs_PES <-
readr::read_csv(file = "data/Fs_PES.csv")## Rows: 8291 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (8): Phylostratum, gamete, 24H, 48H, 1w, 3w, 4w, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fs_PES_F <-
readr::read_csv(file = "data/Fs_PES_F.csv")## Rows: 8291 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (3): Phylostratum, gamete, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fs_PES_M <-
readr::read_csv(file = "data/Fs_PES_M.csv")## Rows: 8291 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (3): Phylostratum, gamete, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Ec_PES_32m <-
readr::read_csv(file = "data/Ec_PES_32m.csv")## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): Phylostratum, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, im...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Ec_PES_25f <-
readr::read_csv(file = "data/Ec_PES_25f.csv")## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): Phylostratum, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, im...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.sqrt-tranformed
Fd_PES.sqrt <-
readr::read_csv(file = "data/Fd_PES.sqrt.csv")## Rows: 7898 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (8): Phylostratum, gamete, E1, E2, E3, E4, E5, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fd_PES_F.sqrt <-
readr::read_csv(file = "data/Fd_PES_F.sqrt.csv")## Rows: 7898 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (2): Phylostratum, V1
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fd_PES_M.sqrt <-
readr::read_csv(file = "data/Fd_PES_M.sqrt.csv")## Rows: 7898 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (2): Phylostratum, V1
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fs_PES.sqrt <-
readr::read_csv(file = "data/Fs_PES.sqrt.csv")## Rows: 8291 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (8): Phylostratum, gamete, 24H, 48H, 1w, 3w, 4w, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fs_PES_F.sqrt <-
readr::read_csv(file = "data/Fs_PES_F.sqrt.csv")## Rows: 8291 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (3): Phylostratum, gamete, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fs_PES_M.sqrt <-
readr::read_csv(file = "data/Fs_PES_M.sqrt.csv")## Rows: 8291 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (3): Phylostratum, gamete, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Ec_PES_32m.sqrt <-
readr::read_csv(file = "data/Ec_PES_32m.sqrt.csv")## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): Phylostratum, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, im...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Ec_PES_25f.sqrt <-
readr::read_csv(file = "data/Ec_PES_25f.sqrt.csv")## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): Phylostratum, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, im...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.log2-tranformed
Fd_PES.log2 <-
readr::read_csv(file = "data/Fd_PES.log2.csv")## Rows: 7898 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (8): Phylostratum, gamete, E1, E2, E3, E4, E5, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fd_PES_F.log2 <-
readr::read_csv(file = "data/Fd_PES_F.log2.csv")## Rows: 7898 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (2): Phylostratum, V1
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fd_PES_M.log2 <-
readr::read_csv(file = "data/Fd_PES_M.log2.csv")## Rows: 7898 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (2): Phylostratum, V1
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fs_PES.log2 <-
readr::read_csv(file = "data/Fs_PES.log2.csv")## Rows: 8291 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (8): Phylostratum, gamete, 24H, 48H, 1w, 3w, 4w, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fs_PES_F.log2 <-
readr::read_csv(file = "data/Fs_PES_F.log2.csv")## Rows: 8291 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (3): Phylostratum, gamete, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fs_PES_M.log2 <-
readr::read_csv(file = "data/Fs_PES_M.log2.csv")## Rows: 8291 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (3): Phylostratum, gamete, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Ec_PES_32m.log2 <-
readr::read_csv(file = "data/Ec_PES_32m.log2.csv")## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): Phylostratum, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, im...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Ec_PES_25f.log2 <-
readr::read_csv(file = "data/Ec_PES_25f.log2.csv")## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): Phylostratum, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, im...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.rank-tranformed
Fd_PES.rank <-
readr::read_csv(file = "data/Fd_PES.rank.csv")## Rows: 7898 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (8): Phylostratum, gamete, E1, E2, E3, E4, E5, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fd_PES_F.rank <-
readr::read_csv(file = "data/Fd_PES_F.rank.csv")## Rows: 7898 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (2): Phylostratum, V1
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fd_PES_M.rank <-
readr::read_csv(file = "data/Fd_PES_M.rank.csv")## Rows: 7898 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (2): Phylostratum, V1
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fs_PES.rank <-
readr::read_csv(file = "data/Fs_PES.rank.csv")## Rows: 8291 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (8): Phylostratum, gamete, 24H, 48H, 1w, 3w, 4w, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fs_PES_F.rank <-
readr::read_csv(file = "data/Fs_PES_F.rank.csv")## Rows: 8291 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (3): Phylostratum, gamete, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fs_PES_M.rank <-
readr::read_csv(file = "data/Fs_PES_M.rank.csv")## Rows: 8291 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (3): Phylostratum, gamete, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Ec_PES_32m.rank <-
readr::read_csv(file = "data/Ec_PES_32m.rank.csv")## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): Phylostratum, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, im...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Ec_PES_25f.rank <-
readr::read_csv(file = "data/Ec_PES_25f.rank.csv")## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): Phylostratum, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, im...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.rlog-tranformed
Fd_PES.rlog <-
readr::read_csv(file = "data/Fd_PES.rlog.csv")## Rows: 7898 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (8): Phylostratum, gamete, E1, E2, E3, E4, E5, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fd_PES_F.rlog <-
readr::read_csv(file = "data/Fd_PES_F.rlog.csv")## Rows: 7898 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (2): Phylostratum, gamete
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fd_PES_M.rlog <-
readr::read_csv(file = "data/Fd_PES_M.rlog.csv")## Rows: 7898 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (2): Phylostratum, gamete
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fs_PES.rlog <-
readr::read_csv(file = "data/Fs_PES.rlog.csv")## Rows: 8291 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (8): Phylostratum, gamete, 24H, 48H, 1w, 3w, 4w, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fs_PES_F.rlog <-
readr::read_csv(file = "data/Fs_PES_F.rlog.csv")## Rows: 8291 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (3): Phylostratum, gamete, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Fs_PES_M.rlog <-
readr::read_csv(file = "data/Fs_PES_M.rlog.csv")## Rows: 8291 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (3): Phylostratum, gamete, matSP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Ec_PES_32m.rlog <-
readr::read_csv(file = "data/Ec_PES_32m.rlog.csv")## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): Phylostratum, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, im...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Ec_PES_25f.rlog <-
readr::read_csv(file = "data/Ec_PES_25f.rlog.csv")## Rows: 11571 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GeneID
## dbl (10): Phylostratum, meiospore, immGA, matGA, oldGA, gamete, earlyPSP, im...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.myTAI issue
I will post an issue on GitHub but myTAI::tf() loses the
column name hwen transforming only one column.
TAI calculation
I had previously used a function to combine males and females into one TAI plot.
#TI stands for transcriptome index
TI.preplot <- function(ExpressionSet, permutations = 50000){
std <-
myTAI::bootMatrix(
ExpressionSet = ExpressionSet,
permutations = permutations) %>%
apply(2, stats::sd)
TI.out <-
myTAI::TAI(ExpressionSet) %>%
tibble::as_tibble(rownames = "Stage", colnames = c("TAI", "PS")) %>%
dplyr::bind_cols(as_tibble(std)) %>%
dplyr::rename(TAI = 2, sd = 3)
return(TI.out)
}Fucus distichus
# function to process Fd. This will be changed to accommodate more seq data.
get_TAI_Fd <- function(PES_all, PES_M, PES_F, ordered_stages){
TAI_b <-
TI.preplot(
dplyr::select(PES_all, !c("gamete"))) %>%
dplyr::mutate(Sex = "Mixed")
stages_to_NA <- # this will differ for Fucus serratus
ordered_stages[-c(1, 1+1)]
TAI_M <-
TI.preplot(
PES_M) %>%
tibble::add_row(
dplyr::filter(
dplyr::select(TAI_b, !Sex),
Stage == ordered_stages[1+1])) %>%
tibble::add_row(
tibble::tibble(Stage = stages_to_NA, TAI = NA, sd = NA)
) %>%
dplyr::mutate(Sex = "Male")
TAI_F <-
TI.preplot(
PES_F) %>%
tibble::add_row(
dplyr::filter(
dplyr::select(TAI_b, !Sex),
Stage == ordered_stages[1+1])) %>%
tibble::add_row(
tibble::tibble(Stage = stages_to_NA, TAI = NA, sd = NA)
) %>%
dplyr::mutate(Sex = "Female") # This is not true - this is needed for the plotting.
TAI_out <-
dplyr::bind_rows(TAI_b, TAI_M, TAI_F)
TAI_out$Stage <- base::factor(TAI_out$Stage, ordered_stages)
return(TAI_out)
}ordered_stages <- colnames(Fd_PES)[3:ncol(Fd_PES)]
Fd_TAI <-
get_TAI_Fd(
PES_all = Fd_PES,
PES_M = Fd_PES_M,
PES_F = Fd_PES_F,
ordered_stages)## New names:
## New names:
## New names:
## • `value` -> `value...2`
## • `value` -> `value...3`Fd_TAI.log2 <-
get_TAI_Fd(
PES_all = Fd_PES.log2,
PES_M = dplyr::rename(Fd_PES_M.log2, gamete = V1),
PES_F = dplyr::rename(Fd_PES_F.log2, gamete = V1),
ordered_stages)## New names:
## New names:
## New names:
## • `value` -> `value...2`
## • `value` -> `value...3`Fd_TAI.sqrt <-
get_TAI_Fd(
PES_all = Fd_PES.sqrt,
PES_M = dplyr::rename(Fd_PES_M.sqrt, gamete = V1),
PES_F = dplyr::rename(Fd_PES_F.sqrt, gamete = V1),
ordered_stages)## New names:
## New names:
## New names:
## • `value` -> `value...2`
## • `value` -> `value...3`Fd_TAI.rank <-
get_TAI_Fd(
PES_all = Fd_PES.rank,
PES_M = dplyr::rename(Fd_PES_M.rank, gamete = V1),
PES_F = dplyr::rename(Fd_PES_F.rank, gamete = V1),
ordered_stages)## New names:
## New names:
## New names:
## • `value` -> `value...2`
## • `value` -> `value...3`Fd_TAI.rlog <-
get_TAI_Fd(
PES_all = Fd_PES.rlog,
PES_M = Fd_PES_M.rlog,
PES_F = Fd_PES_F.rlog,
ordered_stages)## New names:
## New names:
## New names:
## • `value` -> `value...2`
## • `value` -> `value...3`As I previously noted dplyr::rename() is used due to a
bug I found in myTAI.
Fucus serratus
# function to process Fd. This will be changed to accommodate more seq data.
get_TAI_Fs <- function(PES_all, PES_M, PES_F, ordered_stages){
TAI_b <-
TI.preplot(
dplyr::select(PES_all, !c("gamete","matSP"))) %>%
dplyr::mutate(Sex = "Mixed")
stages_to_NA <- # this will differ for Fucus distichus
ordered_stages[-c(1, 1+1, length(ordered_stages) - 1, length(ordered_stages))]
TAI_M <-
TI.preplot(
PES_M) %>%
tibble::add_row(
dplyr::filter(
dplyr::select(TAI_b, !Sex),
Stage %in% c(
ordered_stages[1+1],
ordered_stages[length(ordered_stages) - 1]))) %>%
tibble::add_row(
tibble::tibble(Stage = stages_to_NA, TAI = NA, sd = NA)
) %>%
dplyr::mutate(Sex = "Male")
TAI_F <-
TI.preplot(
PES_F) %>%
tibble::add_row(
dplyr::filter(
dplyr::select(TAI_b, !Sex),
Stage %in% c(
ordered_stages[1+1],
ordered_stages[length(ordered_stages) - 1]))) %>%
tibble::add_row(
tibble::tibble(Stage = stages_to_NA, TAI = NA, sd = NA)
) %>%
dplyr::mutate(Sex = "Female") # This is not true. this is needed for the plotting.
TAI_out <-
dplyr::bind_rows(TAI_b, TAI_M, TAI_F)
TAI_out$Stage <- base::factor(TAI_out$Stage, ordered_stages)
return(TAI_out)
}ordered_stages <- colnames(Fs_PES)[3:ncol(Fs_PES)]
Fs_TAI <-
get_TAI_Fs(
PES_all = Fs_PES,
PES_M = Fs_PES_M,
PES_F = Fs_PES_F,
ordered_stages)## New names:
## New names:
## New names:
## • `value` -> `value...2`
## • `value` -> `value...3`Fs_TAI.log2 <-
get_TAI_Fs(
PES_all = Fs_PES.log2,
PES_M = Fs_PES_M.log2,
PES_F = Fs_PES_F.log2,
ordered_stages)## New names:
## New names:
## New names:
## • `value` -> `value...2`
## • `value` -> `value...3`Fs_TAI.sqrt <-
get_TAI_Fs(
PES_all = Fs_PES.sqrt,
PES_M = Fs_PES_M.sqrt,
PES_F = Fs_PES_F.sqrt,
ordered_stages)## New names:
## New names:
## New names:
## • `value` -> `value...2`
## • `value` -> `value...3`Fs_TAI.rank <-
get_TAI_Fs(
PES_all = Fs_PES.rank,
PES_M = Fs_PES_M.rank,
PES_F = Fs_PES_F.rank,
ordered_stages)## New names:
## New names:
## New names:
## • `value` -> `value...2`
## • `value` -> `value...3`Fs_TAI.rlog <-
get_TAI_Fs(
PES_all = Fs_PES.rlog,
PES_M = Fs_PES_M.rlog,
PES_F = Fs_PES_F.rlog,
ordered_stages)## New names:
## New names:
## New names:
## • `value` -> `value...2`
## • `value` -> `value...3`As I previously noted dplyr::rename() is used due to a
bug I found in myTAI.
Ectocarpus
# function to process Fd. This will be changed to accommodate more seq data.
get_TAI_Ec <- function(PES_M, PES_F, ordered_stages){
TAI_M <-
TI.preplot(PES_M) %>%
dplyr::mutate(Sex = "Male")
TAI_F <-
TI.preplot(PES_F) %>%
dplyr::mutate(Sex = "Female")
stages_to_NA <- # this will differ for Fucus distichus
ordered_stages[-c(1, 1+1, length(ordered_stages) - 1, length(ordered_stages))]
TAI_out <-
dplyr::bind_rows(TAI_M, TAI_F)
TAI_out$Stage <- base::factor(TAI_out$Stage, ordered_stages)
return(TAI_out)
}ordered_stages <- colnames(Ec_PES_25f)[3:ncol(Ec_PES_25f)]
Ec_TAI <-
get_TAI_Ec(
PES_M = Ec_PES_32m,
PES_F = Ec_PES_25f,
ordered_stages)## New names:
## New names:
## • `value` -> `value...2`
## • `value` -> `value...3`Ec_TAI.sqrt <-
get_TAI_Ec(
PES_M = Ec_PES_32m.sqrt,
PES_F = Ec_PES_25f.sqrt,
ordered_stages)## New names:
## New names:
## • `value` -> `value...2`
## • `value` -> `value...3`Ec_TAI.log2 <-
get_TAI_Ec(
PES_M = Ec_PES_32m.log2,
PES_F = Ec_PES_25f.log2,
ordered_stages)## New names:
## New names:
## • `value` -> `value...2`
## • `value` -> `value...3`Ec_TAI.rank <-
get_TAI_Ec(
PES_M = Ec_PES_32m.rank,
PES_F = Ec_PES_25f.rank,
ordered_stages)## New names:
## New names:
## • `value` -> `value...2`
## • `value` -> `value...3`Ec_TAI.rlog <-
get_TAI_Ec(
PES_M = Ec_PES_32m.rlog,
PES_F = Ec_PES_25f.rlog,
ordered_stages)## New names:
## New names:
## • `value` -> `value...2`
## • `value` -> `value...3`TAI profiles
Fucus serratus
Fs_TAI %>%
ggplot2::ggplot(
aes(
y = TAI,
x = Stage,
group = Sex,
colour = Sex,
fill = Sex)) +
ggplot2::geom_ribbon(
aes(ymin = TAI - sd,
ymax = TAI + sd
), alpha = 0.1) +
ggplot2::geom_line(size = 2, lineend = "round") +
theme_classic() +
ggsci::scale_colour_npg() +
labs(title = "Fucus serratus",
subtitle = "TPM")## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Fs_TAI.sqrt %>%
ggplot2::ggplot(
aes(
y = TAI,
x = Stage,
group = Sex,
colour = Sex,
fill = Sex)) +
ggplot2::geom_ribbon(
aes(ymin = TAI - sd,
ymax = TAI + sd
), alpha = 0.1) +
ggplot2::geom_line(size = 2, lineend = "round") +
theme_classic() +
ggsci::scale_colour_npg() +
labs(title = "Fucus serratus",
subtitle = "sqrt(TPM)")
Fs_TAI.log2 %>%
ggplot2::ggplot(
aes(
y = TAI,
x = Stage,
group = Sex,
colour = Sex,
fill = Sex)) +
ggplot2::geom_ribbon(
aes(ymin = TAI - sd,
ymax = TAI + sd
), alpha = 0.1) +
ggplot2::geom_line(size = 2, lineend = "round") +
theme_classic() +
ggsci::scale_colour_npg() +
labs(title = "Fucus serratus",
subtitle = "log2(TPM+\u03b1)")
Fs_TAI.rank %>%
ggplot2::ggplot(
aes(
y = TAI,
x = Stage,
group = Sex,
colour = Sex,
fill = Sex)) +
ggplot2::geom_ribbon(
aes(ymin = TAI - sd,
ymax = TAI + sd
), alpha = 0.1) +
ggplot2::geom_line(size = 2, lineend = "round") +
theme_classic() +
ggsci::scale_colour_npg() +
labs(title = "Fucus serratus",
subtitle = "rank(TPM)")
Fs_TAI.rlog %>%
ggplot2::ggplot(
aes(
y = TAI,
x = Stage,
group = Sex,
colour = Sex,
fill = Sex)) +
ggplot2::geom_ribbon(
aes(ymin = TAI - sd,
ymax = TAI + sd
), alpha = 0.1) +
ggplot2::geom_line(size = 2, lineend = "round") +
theme_classic() +
ggsci::scale_colour_npg() +
labs(title = "Fucus serratus",
subtitle = "rlog(TPM)")
The dynamic range of the rlog transform is really small.
Does this mean that the signal isn’t biologically strong?
Fucus distichus
Fd_TAI %>%
ggplot2::ggplot(
aes(
y = TAI,
x = Stage,
group = Sex,
colour = Sex,
fill = Sex)) +
ggplot2::geom_ribbon(
aes(ymin = TAI - sd,
ymax = TAI + sd
), alpha = 0.1) +
ggplot2::geom_line(size = 2, lineend = "round") +
theme_classic() +
ggsci::scale_colour_npg() +
labs(title = "Fucus distichus",
subtitle = "TPM")## Warning: Removed 10 rows containing missing values (`geom_line()`).
Fd_TAI.sqrt %>%
ggplot2::ggplot(
aes(
y = TAI,
x = Stage,
group = Sex,
colour = Sex,
fill = Sex)) +
ggplot2::geom_ribbon(
aes(ymin = TAI - sd,
ymax = TAI + sd
), alpha = 0.1) +
ggplot2::geom_line(size = 2, lineend = "round") +
theme_classic() +
ggsci::scale_colour_npg() +
labs(title = "Fucus distichus",
subtitle = "sqrt(TPM)")## Warning: Removed 10 rows containing missing values (`geom_line()`).
Fd_TAI.log2 %>%
ggplot2::ggplot(
aes(
y = TAI,
x = Stage,
group = Sex,
colour = Sex,
fill = Sex)) +
ggplot2::geom_ribbon(
aes(ymin = TAI - sd,
ymax = TAI + sd
), alpha = 0.1) +
ggplot2::geom_line(size = 2, lineend = "round") +
theme_classic() +
ggsci::scale_colour_npg() +
labs(title = "Fucus distichus",
subtitle = "log2(TPM+\u03b1)")## Warning: Removed 10 rows containing missing values (`geom_line()`).
Fd_TAI.rank %>%
ggplot2::ggplot(
aes(
y = TAI,
x = Stage,
group = Sex,
colour = Sex,
fill = Sex)) +
ggplot2::geom_ribbon(
aes(ymin = TAI - sd,
ymax = TAI + sd
), alpha = 0.1) +
ggplot2::geom_line(size = 2, lineend = "round") +
theme_classic() +
ggsci::scale_colour_npg() +
labs(title = "Fucus distichus",
subtitle = "rank(TPM)")## Warning: Removed 10 rows containing missing values (`geom_line()`).
Fd_TAI.rlog %>%
ggplot2::ggplot(
aes(
y = TAI,
x = Stage,
group = Sex,
colour = Sex,
fill = Sex)) +
ggplot2::geom_ribbon(
aes(ymin = TAI - sd,
ymax = TAI + sd
), alpha = 0.1) +
ggplot2::geom_line(size = 2, lineend = "round") +
theme_classic() +
ggsci::scale_colour_npg() +
labs(title = "Fucus distichus",
subtitle = "rlog(TPM)")## Warning: Removed 10 rows containing missing values (`geom_line()`).
TAI Fucus embryonic stages only
#Fs
Fs_TAI %>%
dplyr::filter(Stage %in% c("24H", "48H", "1w", "3w", "4w") & Sex == "Mixed") %>%
ggplot2::ggplot(
aes(
y = TAI,
x = Stage,
group = 1)) +
ggplot2::geom_ribbon(
aes(ymin = TAI - sd,
ymax = TAI + sd
),
colour = "#00A087FF",
fill = "#00A087FF",
alpha = 0.1) +
ggplot2::geom_line(
size = 2,
lineend = "round",
colour = "#00A087FF") +
theme_classic() +
ggsci::scale_colour_npg() +
labs(title = "Fucus serratus",
subtitle = "TPM")
Fs_TAI.sqrt %>%
dplyr::filter(Stage %in% c("24H", "48H", "1w", "3w", "4w") & Sex == "Mixed") %>%
ggplot2::ggplot(
aes(
y = TAI,
x = Stage,
group = 1)) +
ggplot2::geom_ribbon(
aes(ymin = TAI - sd,
ymax = TAI + sd
),
colour = "#00A087FF",
fill = "#00A087FF",
alpha = 0.1) +
ggplot2::geom_line(
size = 2,
lineend = "round",
colour = "#00A087FF") +
theme_classic() +
ggsci::scale_colour_npg() +
labs(title = "Fucus serratus",
subtitle = "sqrt(TPM)")
Fs_TAI.log2 %>%
dplyr::filter(Stage %in% c("24H", "48H", "1w", "3w", "4w") & Sex == "Mixed") %>%
ggplot2::ggplot(
aes(
y = TAI,
x = Stage,
group = 1)) +
ggplot2::geom_ribbon(
aes(ymin = TAI - sd,
ymax = TAI + sd
),
colour = "#00A087FF",
fill = "#00A087FF",
alpha = 0.1) +
ggplot2::geom_line(
size = 2,
lineend = "round",
colour = "#00A087FF") +
theme_classic() +
ggsci::scale_colour_npg() +
labs(title = "Fucus serratus",
subtitle = "log2(TPM+\u03b1)")
Fs_TAI.rank %>%
dplyr::filter(Stage %in% c("24H", "48H", "1w", "3w", "4w") & Sex == "Mixed") %>%
ggplot2::ggplot(
aes(
y = TAI,
x = Stage,
group = 1)) +
ggplot2::geom_ribbon(
aes(ymin = TAI - sd,
ymax = TAI + sd
),
colour = "#00A087FF",
fill = "#00A087FF",
alpha = 0.1) +
ggplot2::geom_line(
size = 2,
lineend = "round",
colour = "#00A087FF") +
theme_classic() +
ggsci::scale_colour_npg() +
labs(title = "Fucus serratus",
subtitle = "rank(TPM)")
Fs_TAI.rlog %>%
dplyr::filter(Stage %in% c("24H", "48H", "1w", "3w", "4w") & Sex == "Mixed") %>%
ggplot2::ggplot(
aes(
y = TAI,
x = Stage,
group = 1)) +
ggplot2::geom_ribbon(
aes(ymin = TAI - sd,
ymax = TAI + sd
),
colour = "#00A087FF",
fill = "#00A087FF",
alpha = 0.1) +
ggplot2::geom_line(
size = 2,
lineend = "round",
colour = "#00A087FF") +
theme_classic() +
ggsci::scale_colour_npg() +
labs(title = "Fucus serratus",
subtitle = "rlog(TPM)")
# Fd
Fd_TAI %>%
dplyr::filter(Stage %in% c("E1", "E2", "E3", "E4", "E5") & Sex == "Mixed") %>%
ggplot2::ggplot(
aes(
y = TAI,
x = Stage,
group = 1)) +
ggplot2::geom_ribbon(
aes(ymin = TAI - sd,
ymax = TAI + sd
),
colour = "#00A087FF",
fill = "#00A087FF",
alpha = 0.1) +
ggplot2::geom_line(
size = 2,
lineend = "round",
colour = "#00A087FF") +
theme_classic() +
ggsci::scale_colour_npg() +
labs(title = "Fucus distichus",
subtitle = "TPM")
Fd_TAI.sqrt %>%
dplyr::filter(Stage %in% c("E1", "E2", "E3", "E4", "E5") & Sex == "Mixed") %>%
ggplot2::ggplot(
aes(
y = TAI,
x = Stage,
group = 1)) +
ggplot2::geom_ribbon(
aes(ymin = TAI - sd,
ymax = TAI + sd
),
colour = "#00A087FF",
fill = "#00A087FF",
alpha = 0.1) +
ggplot2::geom_line(
size = 2,
lineend = "round",
colour = "#00A087FF") +
theme_classic() +
ggsci::scale_colour_npg() +
labs(title = "Fucus distichus",
subtitle = "sqrt(TPM)")
Fd_TAI.log2 %>%
dplyr::filter(Stage %in% c("E1", "E2", "E3", "E4", "E5") & Sex == "Mixed") %>%
ggplot2::ggplot(
aes(
y = TAI,
x = Stage,
group = 1)) +
ggplot2::geom_ribbon(
aes(ymin = TAI - sd,
ymax = TAI + sd
),
colour = "#00A087FF",
fill = "#00A087FF",
alpha = 0.1) +
ggplot2::geom_line(
size = 2,
lineend = "round",
colour = "#00A087FF") +
theme_classic() +
ggsci::scale_colour_npg() +
labs(title = "Fucus distichus",
subtitle = "log2(TPM+\u03b1)")
Fd_TAI.rank %>%
dplyr::filter(Stage %in% c("E1", "E2", "E3", "E4", "E5") & Sex == "Mixed") %>%
ggplot2::ggplot(
aes(
y = TAI,
x = Stage,
group = 1)) +
ggplot2::geom_ribbon(
aes(ymin = TAI - sd,
ymax = TAI + sd
),
colour = "#00A087FF",
fill = "#00A087FF",
alpha = 0.1) +
ggplot2::geom_line(
size = 2,
lineend = "round",
colour = "#00A087FF") +
theme_classic() +
ggsci::scale_colour_npg() +
labs(title = "Fucus distichus",
subtitle = "rank(TPM)")
Fd_TAI.rlog %>%
dplyr::filter(Stage %in% c("E1", "E2", "E3", "E4", "E5") & Sex == "Mixed") %>%
ggplot2::ggplot(
aes(
y = TAI,
x = Stage,
group = 1)) +
ggplot2::geom_ribbon(
aes(ymin = TAI - sd,
ymax = TAI + sd
),
colour = "#00A087FF",
fill = "#00A087FF",
alpha = 0.1) +
ggplot2::geom_line(
size = 2,
lineend = "round",
colour = "#00A087FF") +
theme_classic() +
ggsci::scale_colour_npg() +
labs(title = "Fucus distichus",
subtitle = "rlog(TPM)")
Ectocarpus
Ec_TAI %>%
ggplot2::ggplot(
aes(
y = TAI,
x = Stage,
group = Sex,
colour = Sex,
fill = Sex)) +
ggplot2::geom_ribbon(
aes(ymin = TAI - sd,
ymax = TAI + sd
), alpha = 0.1) +
ggplot2::geom_line(size = 2, lineend = "round") +
theme_classic() +
ggsci::scale_colour_npg() +
ggplot2::scale_x_discrete(guide = ggplot2::guide_axis(n.dodge=2)) +
labs(title = "Ectocarpus",
subtitle = "TPM")
Ec_TAI.sqrt %>%
ggplot2::ggplot(
aes(
y = TAI,
x = Stage,
group = Sex,
colour = Sex,
fill = Sex)) +
ggplot2::geom_ribbon(
aes(ymin = TAI - sd,
ymax = TAI + sd
), alpha = 0.1) +
ggplot2::geom_line(size = 2, lineend = "round") +
theme_classic() +
ggsci::scale_colour_npg() +
ggplot2::scale_x_discrete(guide = ggplot2::guide_axis(n.dodge=2)) +
labs(title = "Ectocarpus",
subtitle = "sqrt(TPM)")
Ec_TAI.log2 %>%
ggplot2::ggplot(
aes(
y = TAI,
x = Stage,
group = Sex,
colour = Sex,
fill = Sex)) +
ggplot2::geom_ribbon(
aes(ymin = TAI - sd,
ymax = TAI + sd
), alpha = 0.1) +
ggplot2::geom_line(size = 2, lineend = "round") +
theme_classic() +
ggsci::scale_colour_npg() +
ggplot2::scale_x_discrete(guide = ggplot2::guide_axis(n.dodge=2)) +
labs(title = "Ectocarpus",
subtitle = "log2(TPM+\u03b1)")
Ec_TAI.rank %>%
ggplot2::ggplot(
aes(
y = TAI,
x = Stage,
group = Sex,
colour = Sex,
fill = Sex)) +
ggplot2::geom_ribbon(
aes(ymin = TAI - sd,
ymax = TAI + sd
), alpha = 0.1) +
ggplot2::geom_line(size = 2, lineend = "round") +
theme_classic() +
ggsci::scale_colour_npg() +
ggplot2::scale_x_discrete(guide = ggplot2::guide_axis(n.dodge=2)) +
labs(title = "Ectocarpus",
subtitle = "rank(TPM)")
Ec_TAI.rlog %>%
ggplot2::ggplot(
aes(
y = TAI,
x = Stage,
group = Sex,
colour = Sex,
fill = Sex)) +
ggplot2::geom_ribbon(
aes(ymin = TAI - sd,
ymax = TAI + sd
), alpha = 0.1) +
ggplot2::geom_line(size = 2, lineend = "round") +
theme_classic() +
ggsci::scale_colour_npg() +
ggplot2::scale_x_discrete(guide = ggplot2::guide_axis(n.dodge=2)) +
labs(title = "Ectocarpus",
subtitle = "rlog(TPM)")
The r-log is behaving weird because the transformation is non-linear but also does not preserve the monotonic relationship.
tfStability
Flat line test
I will later redo with 50 000 permutations but I will use 20 000 for now since I get an error
Error in gamma_MME[[3]] : subscript out of bounds.
I have redone this and it works without error.
F. serratus
Fs_PES_tS <-
tfStability(
dplyr::select(Fs_PES, !c(gamete, matSP)),
transforms = c("none", "sqrt", "log2", "rank"),
permutations = 50000
)
Fs_PES_tS_processed <-
tibble::as_tibble(Fs_PES_tS, rownames = "transformation")
Fs_PES_tS.rlog <-
tfStability(
dplyr::select(Fs_PES.rlog, !c(gamete, matSP)),
transforms = c("none"), # it is already transformed.
permutations = 50000
)
Fs_PES_tS.rlog_processed <-
tibble::as_tibble(Fs_PES_tS.rlog, rownames = "transformation") %>%
dplyr::mutate(transformation = "rlog")
Fs_PES_tS_res <-
dplyr::bind_rows(Fs_PES_tS_processed, Fs_PES_tS.rlog_processed) %>%
dplyr::rename("pval" = value) %>%
dplyr::mutate(test = "FlatLineTest")F. distichus
Fd_PES_tS <-
tfStability(
dplyr::select(Fd_PES, !c(gamete, matSP)),
transforms = c("none", "sqrt", "log2", "rank"),
permutations = 50000
)
Fd_PES_tS_processed <-
tibble::as_tibble(Fd_PES_tS, rownames = "transformation")
Fd_PES_tS.rlog <-
tfStability(
dplyr::select(Fd_PES.rlog, !c(gamete, matSP)),
transforms = c("none"), # it is already transformed.
permutations = 50000
)
Fd_PES_tS.rlog_processed <-
tibble::as_tibble(Fd_PES_tS.rlog, rownames = "transformation") %>%
dplyr::mutate(transformation = "rlog")
Fd_PES_tS_res <-
dplyr::bind_rows(Fd_PES_tS_processed, Fd_PES_tS.rlog_processed) %>%
dplyr::rename("pval" = value) %>%
dplyr::mutate(test = "FlatLineTest")Ectocarpus
# female
Ec_PES_25f_tS <-
tfStability(
Ec_PES_25f,
transforms = c("none", "sqrt", "log2", "rank"),
permutations = 50000
)
Ec_PES_25f_tS_processed <-
tibble::as_tibble(Ec_PES_25f_tS, rownames = "transformation")
Ec_PES_25f_tS.rlog <-
tfStability(
Ec_PES_25f.rlog,
transforms = c("none"), # it is already transformed.
permutations = 50000
)
Ec_PES_25f_tS.rlog_processed <-
tibble::as_tibble(Ec_PES_25f_tS.rlog, rownames = "transformation") %>%
dplyr::mutate(transformation = "rlog")
Ec_PES_25f_tS_res <-
dplyr::bind_rows(Ec_PES_25f_tS_processed, Ec_PES_25f_tS.rlog_processed) %>%
dplyr::rename("pval" = value) %>%
dplyr::mutate(test = "FlatLineTest")
# male
Ec_PES_32m_tS <-
tfStability(
Ec_PES_32m,
transforms = c("none", "sqrt", "log2", "rank"),
permutations = 50000
)
Ec_PES_32m_tS_processed <-
tibble::as_tibble(Ec_PES_32m_tS, rownames = "transformation")
Ec_PES_32m_tS.rlog <-
tfStability(
Ec_PES_32m.rlog,
transforms = c("none"), # it is already transformed.
permutations = 50000
)
Ec_PES_32m_tS.rlog_processed <-
tibble::as_tibble(Ec_PES_32m_tS.rlog, rownames = "transformation") %>%
dplyr::mutate(transformation = "rlog")
Ec_PES_32m_tS_res <-
dplyr::bind_rows(Ec_PES_32m_tS_processed, Ec_PES_32m_tS.rlog_processed) %>%
dplyr::rename("pval" = value) %>%
dplyr::mutate(test = "FlatLineTest")Plot p-values
Fucus serratus development
Fs_PES_tS_res %>%
ggplot2::ggplot(
aes(
y = -log10(pval),
x = transformation)) +
ggplot2::geom_col(width = 0.05) +
ggplot2::geom_point(size = 5) +
ggplot2::geom_hline(
yintercept = -log10(0.05),
colour = "#E64B35FF",
linetype = 'dashed',
size = 2
) +
ggplot2::annotate(
"label",
x = 0.7,
y = max(-log10(Fs_PES_tS_res$pval))/6,
label = "pval < 0.05",
# angle = 270,
colour = "#E64B35FF") +
ggplot2::labs(
title = "Fucus serratus",
subtitle = "FlatLineTest",
x = "Transformation"
) +
ggplot2::coord_flip() +
ggplot2::theme_classic()
# or perhaps we can use raw Pvalues
Fs_PES_tS_res %>%
ggplot2::ggplot(
aes(
y = pval,
x = transformation)) +
ggplot2::geom_col(width = 0.05) +
ggplot2::geom_point(size = 5) +
ggplot2::geom_hline(
yintercept = 0.05,
colour = "#E64B35FF",
linetype = 'dashed',
size = 2
) +
ggplot2::annotate(
"label",
x = 1.5,
y = 0.05 - 0.005,
label = "pval < 0.05",
# angle = 270,
colour = "#E64B35FF") +
ggplot2::labs(
title = "Fucus serratus",
subtitle = "FlatLineTest",
x = "Transformation"
) +
ggplot2::coord_flip() +
ggplot2::theme_classic()
Fucus distichus development
Fd_PES_tS_res %>%
ggplot2::ggplot(
aes(
y = -log10(pval),
x = transformation)) +
ggplot2::geom_col(width = 0.05) +
ggplot2::geom_point(size = 5) +
ggplot2::geom_hline(
yintercept = -log10(0.05),
colour = "#E64B35FF",
linetype = 'dashed',
size = 2
) +
ggplot2::annotate(
"label",
x = 0.7,
y = max(-log10(Fd_PES_tS_res$pval))/6,
label = "pval < 0.05",
# angle = 270,
colour = "#E64B35FF") +
ggplot2::labs(
title = "Fucus distichus",
subtitle = "FlatLineTest",
x = "Transformation"
) +
ggplot2::coord_flip() +
ggplot2::theme_classic()
# or perhaps we can use raw Pvalues
Fd_PES_tS_res %>%
ggplot2::ggplot(
aes(
y = pval,
x = transformation)) +
ggplot2::geom_col(width = 0.05) +
ggplot2::geom_point(size = 5) +
ggplot2::geom_hline(
yintercept = 0.05,
colour = "#E64B35FF",
linetype = 'dashed',
size = 2
) +
ggplot2::annotate(
"label",
x = 1.5,
y = 0.05 - 0.005,
label = "pval < 0.05",
# angle = 270,
colour = "#E64B35FF") +
ggplot2::labs(
title = "Fucus distichus",
subtitle = "FlatLineTest",
x = "Transformation"
) +
ggplot2::coord_flip() +
ggplot2::theme_classic()
Ec_PES_32m_tS_res %>%
ggplot2::ggplot(
aes(
y = -log10(pval),
x = transformation)) +
ggplot2::geom_col(width = 0.05) +
ggplot2::geom_point(size = 5) +
ggplot2::geom_hline(
yintercept = -log10(0.05),
colour = "#E64B35FF",
linetype = 'dashed',
size = 2
) +
ggplot2::annotate(
"label",
x = 0.7,
y = max(-log10(Ec_PES_32m_tS_res$pval))/6,
label = "pval < 0.05",
# angle = 270,
colour = "#E64B35FF") +
ggplot2::labs(
title = "Ectocarpus (male)",
subtitle = "FlatLineTest",
x = "Transformation"
) +
ggplot2::coord_flip() +
ggplot2::theme_classic()
Ec_PES_32m_tS_res %>%
ggplot2::ggplot(
aes(
y = pval,
x = transformation)) +
ggplot2::geom_col(width = 0.05) +
ggplot2::geom_point(size = 5) +
ggplot2::geom_hline(
yintercept = 0.05,
colour = "#E64B35FF",
linetype = 'dashed',
size = 2
) +
ggplot2::annotate(
"label",
x = 1.5,
y = 0.05 - 0.005,
label = "pval < 0.05",
# angle = 270,
colour = "#E64B35FF") +
ggplot2::labs(
title = "Ectocarpus (male)",
subtitle = "FlatLineTest",
x = "Transformation"
) +
ggplot2::coord_flip() +
ggplot2::theme_classic()
Ec_PES_25f_tS_res %>%
ggplot2::ggplot(
aes(
y = -log10(pval),
x = transformation)) +
ggplot2::geom_col(width = 0.05) +
ggplot2::geom_point(size = 5) +
ggplot2::geom_hline(
yintercept = -log10(0.05),
colour = "#E64B35FF",
linetype = 'dashed',
size = 2
) +
ggplot2::annotate(
"label",
x = 0.7,
y = max(-log10(Ec_PES_25f_tS_res$pval))/6,
label = "pval < 0.05",
# angle = 270,
colour = "#E64B35FF") +
ggplot2::labs(
title = "Ectocarpus (female)",
subtitle = "FlatLineTest",
x = "Transformation"
) +
ggplot2::coord_flip() +
ggplot2::theme_classic()
Ec_PES_25f_tS_res %>%
ggplot2::ggplot(
aes(
y = pval,
x = transformation)) +
ggplot2::geom_col(width = 0.05) +
ggplot2::geom_point(size = 5) +
ggplot2::geom_hline(
yintercept = 0.05,
colour = "#E64B35FF",
linetype = 'dashed',
size = 2
) +
ggplot2::annotate(
"label",
x = 1.5,
y = 0.05 - 0.005,
label = "pval < 0.05",
# angle = 270,
colour = "#E64B35FF") +
ggplot2::labs(
title = "Ectocarpus (female)",
subtitle = "FlatLineTest",
x = "Transformation"
) +
ggplot2::coord_flip() +
ggplot2::theme_classic()
Non-flat line profile
Fs_PES_tS <-
tfStability(
dplyr::select(Fs_PES, !c(gamete, matSP)),
TestStatistic = "ReductiveHourglassTest",
transforms = c("none", "sqrt", "log2", "rank"),
modules = list(early = 1:2, mid = 3:4, late = 5),
permutations = 50000
)## Proceeding with the ReductiveHourglassTestFs_PES_tS_processed <-
tibble::as_tibble(Fs_PES_tS, rownames = "transformation")
Fs_PES_tS.rlog <-
tfStability(
dplyr::select(Fs_PES.rlog, !c(gamete, matSP)),
TestStatistic = "ReductiveHourglassTest",
transforms = c("none"), # it is already transformed.
modules = list(early = 1:2, mid = 3:4, late = 5),
permutations = 50000
)## Proceeding with the ReductiveHourglassTestFs_PES_tS.rlog_processed <-
tibble::as_tibble(Fs_PES_tS.rlog, rownames = "transformation") %>%
dplyr::mutate(transformation = "rlog")
Fs_PES_tS_res <-
dplyr::bind_rows(Fs_PES_tS_processed, Fs_PES_tS.rlog_processed) %>%
dplyr::rename("pval" = value) %>%
dplyr::mutate(test = "ReductiveHourglassTest")
Fd_PES_tS <-
tfStability(
dplyr::select(Fd_PES, !c(gamete, matSP)),
TestStatistic = "LateConservationTest",
transforms = c("none", "sqrt", "log2", "rank"),
modules = list(early = 1:2, mid = 3:4, late = 5),
permutations = 50000
)## Proceeding with the LateConservationTestFd_PES_tS_processed <-
tibble::as_tibble(Fd_PES_tS, rownames = "transformation")
Fd_PES_tS.rlog <-
tfStability(
dplyr::select(Fd_PES.rlog, !c(gamete, matSP)),
TestStatistic = "LateConservationTest",
transforms = c("none"), # it is already transformed.
modules = list(early = 1:2, mid = 3:4, late = 5),
permutations = 50000
)## Proceeding with the LateConservationTestFd_PES_tS.rlog_processed <-
tibble::as_tibble(Fd_PES_tS.rlog, rownames = "transformation") %>%
dplyr::mutate(transformation = "rlog")
Fd_PES_tS_res <-
dplyr::bind_rows(Fd_PES_tS_processed, Fd_PES_tS.rlog_processed) %>%
dplyr::rename("pval" = value) %>%
dplyr::mutate(test = "LateConservationTest")Plot p-values
Fucus serratus development
Fs_PES_tS_res %>%
ggplot2::ggplot(
aes(
y = -log10(pval),
x = transformation)) +
ggplot2::geom_col(width = 0.05) +
ggplot2::geom_point(size = 5) +
ggplot2::geom_hline(
yintercept = -log10(0.05),
colour = "#E64B35FF",
linetype = 'dashed',
size = 2
) +
ggplot2::annotate(
"label",
x = 0.7,
y = max(-log10(Fs_PES_tS_res$pval))/6,
label = "pval < 0.05",
# angle = 270,
colour = "#E64B35FF") +
ggplot2::labs(
title = "Fucus serratus",
subtitle = "ReductiveHourglassTest",
x = "Transformation"
) +
ggplot2::coord_flip() +
ggplot2::theme_classic()
# or perhaps we can use raw Pvalues
Fs_PES_tS_res %>%
ggplot2::ggplot(
aes(
y = pval,
x = transformation)) +
ggplot2::geom_col(width = 0.05) +
ggplot2::geom_point(size = 5) +
ggplot2::geom_hline(
yintercept = 0.05,
colour = "#E64B35FF",
linetype = 'dashed',
size = 2
) +
ggplot2::annotate(
"label",
x = 1.5,
y = 0.05 - 0.005,
label = "pval < 0.05",
# angle = 270,
colour = "#E64B35FF") +
ggplot2::labs(
title = "Fucus serratus",
subtitle = "ReductiveHourglassTest",
x = "Transformation"
) +
ggplot2::coord_flip() +
ggplot2::theme_classic()
Fucus distichus development
Fd_PES_tS_res %>%
ggplot2::ggplot(
aes(
y = -log10(pval),
x = transformation)) +
ggplot2::geom_col(width = 0.05) +
ggplot2::geom_point(size = 5) +
ggplot2::geom_hline(
yintercept = -log10(0.05),
colour = "#E64B35FF",
linetype = 'dashed',
size = 2
) +
ggplot2::annotate(
"label",
x = 0.7,
y = max(-log10(Fd_PES_tS_res$pval))/6,
label = "pval < 0.05",
# angle = 270,
colour = "#E64B35FF") +
ggplot2::labs(
title = "Fucus distichus",
subtitle = "LateConservationTest",
x = "Transformation"
) +
ggplot2::coord_flip() +
ggplot2::theme_classic()
# y = -log10(0.05) + max(Fd_PES_tS_res)/9,
# or perhaps we can use raw Pvalues
Fd_PES_tS_res %>%
ggplot2::ggplot(
aes(
y = pval,
x = transformation)) +
ggplot2::geom_col(width = 0.05) +
ggplot2::geom_point(size = 5) +
ggplot2::geom_hline(
yintercept = 0.05,
colour = "#E64B35FF",
linetype = 'dashed',
size = 2
) +
ggplot2::annotate(
"label",
x = 1.5,
y = 0.05 - 0.005,
label = "pval < 0.05",
# angle = 270,
colour = "#E64B35FF") +
ggplot2::labs(
title = "Fucus distichus",
subtitle = "LateConservationTest",
x = "Transformation"
) +
ggplot2::coord_flip() +
ggplot2::theme_classic()
Get session info.
devtools::session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.2.2 (2022-10-31)
## os macOS Big Sur ... 10.16
## system x86_64, darwin17.0
## ui X11
## language (EN)
## collate en_GB.UTF-8
## ctype en_GB.UTF-8
## tz Europe/Berlin
## date 2023-08-29
## pandoc 2.19.2 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/tools/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date (UTC) lib source
## bit 4.0.5 2022-11-15 [1] CRAN (R 4.2.0)
## bit64 4.0.5 2020-08-30 [1] CRAN (R 4.2.0)
## bslib 0.5.1 2023-08-11 [1] CRAN (R 4.2.0)
## cachem 1.0.8 2023-05-01 [1] CRAN (R 4.2.0)
## callr 3.7.3 2022-11-02 [1] CRAN (R 4.2.0)
## cli 3.6.1 2023-03-23 [1] CRAN (R 4.2.0)
## codetools 0.2-19 2023-02-01 [1] CRAN (R 4.2.0)
## colorspace 2.1-0 2023-01-23 [1] CRAN (R 4.2.0)
## crayon 1.5.2 2022-09-29 [1] CRAN (R 4.2.0)
## devtools 2.4.5 2022-10-11 [1] CRAN (R 4.2.0)
## digest 0.6.33 2023-07-07 [1] CRAN (R 4.2.0)
## dplyr * 1.1.2 2023-04-20 [1] CRAN (R 4.2.0)
## ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.2.0)
## evaluate 0.21 2023-05-05 [1] CRAN (R 4.2.0)
## fansi 1.0.4 2023-01-22 [1] CRAN (R 4.2.0)
## farver 2.1.1 2022-07-06 [1] CRAN (R 4.2.0)
## fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.2.0)
## fitdistrplus 1.1-11 2023-04-25 [1] CRAN (R 4.2.0)
## forcats * 1.0.0 2023-01-29 [1] CRAN (R 4.2.0)
## foreach 1.5.2 2022-02-02 [1] CRAN (R 4.2.0)
## fs 1.6.3 2023-07-20 [1] CRAN (R 4.2.0)
## generics 0.1.3 2022-07-05 [1] CRAN (R 4.2.0)
## ggplot2 * 3.4.3 2023-08-14 [1] CRAN (R 4.2.0)
## ggsci 3.0.0 2023-03-08 [1] CRAN (R 4.2.0)
## glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.0)
## gtable 0.3.4 2023-08-21 [1] CRAN (R 4.2.0)
## highr 0.10 2022-12-22 [1] CRAN (R 4.2.0)
## hms 1.1.3 2023-03-21 [1] CRAN (R 4.2.0)
## htmltools 0.5.6 2023-08-10 [1] CRAN (R 4.2.0)
## htmlwidgets 1.6.2 2023-03-17 [1] CRAN (R 4.2.0)
## httpuv 1.6.11 2023-05-11 [1] CRAN (R 4.2.2)
## iterators 1.0.14 2022-02-05 [1] CRAN (R 4.2.0)
## jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.2.0)
## jsonlite 1.8.7 2023-06-29 [1] CRAN (R 4.2.0)
## knitr 1.43 2023-05-25 [1] CRAN (R 4.2.2)
## labeling 0.4.2 2020-10-20 [1] CRAN (R 4.2.0)
## later 1.3.1 2023-05-02 [1] CRAN (R 4.2.2)
## lattice 0.21-8 2023-04-05 [1] CRAN (R 4.2.0)
## lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.2.0)
## lubridate * 1.9.2 2023-02-10 [1] CRAN (R 4.2.0)
## magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.0)
## MASS 7.3-60 2023-05-04 [1] CRAN (R 4.2.2)
## Matrix 1.5-4.1 2023-05-18 [1] CRAN (R 4.2.0)
## memoise 2.0.1 2021-11-26 [1] CRAN (R 4.2.0)
## mime 0.12 2021-09-28 [1] CRAN (R 4.2.0)
## miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.2.0)
## munsell 0.5.0 2018-06-12 [1] CRAN (R 4.2.0)
## myTAI * 1.0.1.9000 2023-08-29 [1] Github (drostlab/myTAI@699b78f)
## pillar 1.9.0 2023-03-22 [1] CRAN (R 4.2.0)
## pkgbuild 1.4.2 2023-06-26 [1] CRAN (R 4.2.0)
## pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.0)
## pkgload 1.3.2.1 2023-07-08 [1] CRAN (R 4.2.0)
## prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.2.0)
## processx 3.8.2 2023-06-30 [1] CRAN (R 4.2.0)
## profvis 0.3.8 2023-05-02 [1] CRAN (R 4.2.0)
## promises 1.2.1 2023-08-10 [1] CRAN (R 4.2.2)
## ps 1.7.5 2023-04-18 [1] CRAN (R 4.2.0)
## purrr * 1.0.2 2023-08-10 [1] CRAN (R 4.2.2)
## R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.0)
## Rcpp 1.0.11 2023-07-06 [1] CRAN (R 4.2.0)
## readr * 2.1.4 2023-02-10 [1] CRAN (R 4.2.0)
## remotes 2.4.2.1 2023-07-18 [1] CRAN (R 4.2.2)
## rlang 1.1.1 2023-04-28 [1] CRAN (R 4.2.0)
## rmarkdown 2.24 2023-08-14 [1] CRAN (R 4.2.0)
## rstudioapi 0.15.0 2023-07-07 [1] CRAN (R 4.2.0)
## sass 0.4.7 2023-07-15 [1] CRAN (R 4.2.0)
## scales 1.2.1 2022-08-20 [1] CRAN (R 4.2.0)
## sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.0)
## shiny 1.7.5 2023-08-12 [1] CRAN (R 4.2.0)
## stringi 1.7.12 2023-01-11 [1] CRAN (R 4.2.0)
## stringr * 1.5.0 2022-12-02 [1] CRAN (R 4.2.0)
## survival 3.5-7 2023-08-14 [1] CRAN (R 4.2.0)
## tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.2.0)
## tidyr * 1.3.0 2023-01-24 [1] CRAN (R 4.2.0)
## tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.2.0)
## tidyverse * 2.0.0 2023-02-22 [1] CRAN (R 4.2.0)
## timechange 0.2.0 2023-01-11 [1] CRAN (R 4.2.0)
## tzdb 0.4.0 2023-05-12 [1] CRAN (R 4.2.2)
## urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.2.0)
## usethis 2.2.2 2023-07-06 [1] CRAN (R 4.2.0)
## utf8 1.2.3 2023-01-31 [1] CRAN (R 4.2.0)
## vctrs 0.6.3 2023-06-14 [1] CRAN (R 4.2.0)
## vroom 1.6.3 2023-04-28 [1] CRAN (R 4.2.0)
## withr 2.5.0 2022-03-03 [1] CRAN (R 4.2.0)
## xfun 0.40 2023-08-09 [1] CRAN (R 4.2.2)
## xtable 1.8-4 2019-04-21 [1] CRAN (R 4.2.0)
## yaml 2.3.7 2023-01-23 [1] CRAN (R 4.2.0)
##
## [1] /Library/Frameworks/R.framework/Versions/4.2/Resources/library
##
## ──────────────────────────────────────────────────────────────────────────────